This study uses Nihommatsu city and Mimanisoma City as case studies. The two cities are 70km and 30km from Fukushima Daichi Plant respectively.
library("leaflet")
library(readr)
library(dplyr)
library(RColorBrewer)
library(Hmisc)
NB:Important Notes from the Nuclear Regulation Authority on JAEA website
Purposes of this vehicle survey were: 1. To ascertain the tendency and cause of time change of air dose rates by comparing past vehicle survey data and survey meter data at the height of 1 m above ground as well as “walk survey” data, and 2. To contribute to the establishment of radioactive substances distribution prediction model. MEXT evaluated the decrease in the air dose rates caused by the decay of cesium during the survey period and it was around 1%, which was smaller than the errors of measuring instruments source
niho <- read_csv("jun_2011_fukushima.csv")
niho2013 <- read_csv("nov_2013_fukushima.csv")
# View(niho2013)
# names(niho)
dim(niho)
## [1] 45273 18
# head(niho)
class(niho)
## [1] "tbl_df" "tbl" "data.frame"
names(niho) <- c("gridcode","pref","city","gridCenterNorthlat","gridCenterEastlng","gridCenterNorthlatDec",
"gridCenterEastlngDec","daichi_distance","no_samples","AvgAirDoseRate",
"NE_nLat","NE_eLong","NW_nLat","NW_eLong",
"SW_nLat","SW_eLong","SE_nLat","SE_eLong")
names(niho2013) <- c("gridcode","pref","city","gridCenterNorthlat","gridCenterEastlng","gridCenterNorthlatDec",
"gridCenterEastlngDec","daichi_distance","no_samples","AvgAirDoseRate",
"NE_nLat","NE_eLong","NW_nLat","NW_eLong",
"SW_nLat","SW_eLong","SE_nLat","SE_eLong")
#Strip Nihommatsu city,
niho$city[niho$city == "Nihommatsu city"] <- "nihommatsu"
niho2013$city[niho2013$city == "Nihommatsu city"] <- "nihommatsu"
nihom <- subset(niho, city == "nihommatsu")
nihom2013 <- subset(niho2013, city == "nihommatsu")
dim(nihom)
## [1] 2944 18
dim(nihom2013)
## [1] 11758 18
# View(nihom2013)
class(nihom)
## [1] "tbl_df" "data.frame"
niho_q <- nihom %>%
mutate(dose_quants = cut2(nihom$AvgAirDoseRate,cuts=c(0.1,0.5,1.0,1.5,2.0,2.5,3.0),levels.mean=TRUE))
# View(niho_q)
niho_q <- na.omit(niho_q)
# write_csv(niho_q, path = "niho_q.csv")
nihom2013_q <- nihom2013 %>%
mutate(dose_quants = cut2(nihom2013$AvgAirDoseRate,cuts=seq(0.06,1.6,0.25),levels.mean=TRUE))
summary(nihom2013$AvgAirDoseRate)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0630 0.2800 0.3700 0.4009 0.4900 1.4000
summary(niho_q$AvgAirDoseRate)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.190 0.710 1.000 1.029 1.300 2.500
iro <- colorFactor(
palette = "YlOrRd",
domain = niho_q$dose_quants
)
iro2013 <- colorFactor(
palette = "YlOrRd",
domain = nihom2013_q$dose_quants
)
# Link of Daichi
fukulink <- paste(sep = "<br/>",
"<br><a href='http://www.tepco.co.jp/en/decommision/index-e.html'>Fukushima Daichi</a></b>",
"Source of radiations"
)
niho_plot <- leaflet() %>%
addTiles()%>%
addRectangles(data = niho_q,lng1 = ~SW_eLong, lat1 = ~SW_nLat,lng2 = ~NE_eLong, lat2 = ~NE_nLat,
color = ~iro(niho_q$dose_quants))%>%
addLegend("bottomright", pal = iro, values = niho_q$dose_quants,
title = "AvgAirDoseRates",
labFormat = labelFormat(prefix = "µSv/h "),
opacity = 1)%>%
addPopups(lat = 37.4211, lng = 141.0328,popup = fukulink,
options = popupOptions(closeButton = TRUE))
niho_plot
niho2013_plot <- leaflet() %>%
addTiles()%>%
addRectangles(data = nihom2013_q,lng1 = ~SW_eLong, lat1 = ~SW_nLat,lng2 = ~NE_eLong, lat2 = ~NE_nLat,
color = ~iro2013(nihom2013_q$dose_quants))%>%
addLegend("bottomright", pal = iro2013, values = nihom2013_q$dose_quants,
title = "AvgAirDoseRates",
labFormat = labelFormat(prefix = "µSv/h "),
opacity = 1)%>%
addPopups(lat = 37.4211, lng = 141.0328,popup = fukulink,
options = popupOptions(closeButton = TRUE))
niho2013_plot
ggplot(niho_q, aes(daichi_distance,AvgAirDoseRate)) +
geom_point() +
geom_smooth(se = FALSE)+
ggtitle("AvgAirDose against Distance to Daichi Plant")
ggplot(data = niho_q) +
geom_bar(mapping = aes(x = daichi_distance, fill = dose_quants), width = 1)+
ggtitle("AvgAirDose Measured Counts against Daichi Distance")
mina <- read_csv("niho.csv")
dim(mina)
## [1] 45273 18
# View(mina)
#Select Minamisoma City only
mina$city[mina$city == "Minamisoma city"] <- "minamisoma"
#filter nihomatsu observations only
mina <- subset(mina, city == "minamisoma")
dim(mina)
## [1] 1985 18
summary(mina$AvgAirDoseRate)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.210 0.520 0.780 1.771 1.800 18.000
#plot(mina$AvgAirDoseRate)
mina_q <- mina %>%
mutate(mina_quants = cut2(mina$AvgAirDoseRate,cuts=seq(0.2,20,2.2),levels.mean=TRUE))
## View(mina_q)
mina_q <- na.omit(mina_q)
write_csv(mina_q, path = "mina_q.csv")
mina_iro <- colorFactor(
palette = "Set1",
domain = mina_q$mina_quants
)
mina_plot <- leaflet() %>%
addTiles()%>%
addRectangles(data = mina_q,lng1 = ~SW_eLong, lat1 = ~SW_nLat,lng2 = ~NE_eLong, lat2 = ~NE_nLat,
color = ~mina_iro(mina_q$mina_quants))%>%
addLegend("bottomright", pal = mina_iro, values = mina_q$mina_quants,
title = "AvgAirDoseRates",
labFormat = labelFormat(prefix = "µSv/h "),
opacity = 1)%>%
addPopups(lat = 37.4211, lng = 141.0328,popup = fukulink,
options = popupOptions(closeButton = TRUE))
mina_plot
ggplot(mina_q, aes(daichi_distance,AvgAirDoseRate)) +
geom_point() +
geom_smooth(se = FALSE)+
ggtitle("AvgAirDose against Distance to Daichi Plant")
ggplot(data = mina_q) +
geom_bar(mapping = aes(x = daichi_distance, fill = mina_quants), width = 1)+
ggtitle("Counts of Measuring Locations per Air Dose Rate")
library(ggplot2)
niho_q <- read_csv("niho_q.csv")
niho2013_q <- read_csv("niho2013.csv")
names(niho_q) <- c("gridcode","pref","city","gridCenterNorthlat","gridCenterEastlng","gridCenterNorthlatDec",
"gridCenterEastlngDec","daichi_distance","no_samples","AvgAirDoseRate2011",
"NE_nLat","NE_eLong","NW_nLat","NW_eLong",
"SW_nLat","SW_eLong","SE_nLat","SE_eLong")
names(niho2013_q) <- c("gridcode","pref","city","gridCenterNorthlat","gridCenterEastlng","gridCenterNorthlatDec",
"gridCenterEastlngDec","daichi_distance","no_samples","AvgAirDoseRate2013",
"NE_nLat","NE_eLong","NW_nLat","NW_eLong",
"SW_nLat","SW_eLong","SE_nLat","SE_eLong")
niho11_13 <- merge(niho_q, niho2013_q, by.x = "gridcode", by.y = "gridcode", all = TRUE)
fit1 <- lm(AvgAirDoseRate2011~AvgAirDoseRate2013, data = niho11_13)
summary(fit1)
##
## Call:
## lm(formula = AvgAirDoseRate2011 ~ AvgAirDoseRate2013, data = niho11_13)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.87112 -0.13497 -0.01782 0.11937 0.98218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.245108 0.009633 25.45 <2e-16 ***
## AvgAirDoseRate2013 2.437988 0.027364 89.10 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2051 on 2805 degrees of freedom
## (9088 observations deleted due to missingness)
## Multiple R-squared: 0.7389, Adjusted R-squared: 0.7388
## F-statistic: 7938 on 1 and 2805 DF, p-value: < 2.2e-16
ggplot(niho11_13, aes(x = AvgAirDoseRate2011, y = AvgAirDoseRate2013)) +
geom_point() +
stat_smooth(method = "lm", col = "red")+
ggtitle("Correlationg between Air Dose Rate of 2011 vs of 2013")
End